Hi Siobhan,
I'm not the greatest expert on this but making the DISTRIBUTION look the
same is exactly what normalisation (across chips) is meant to do. That is
not to say that the EXPRESSION EFFECTS are lost. There are many explanations
around but for me the penny dropped best after seeing the explanation on
quantile normalisation in the PLIER presentation from affymetrix (requires
registration but here's link:
https://www.affymetrix.com/support/learning/expression_data/eda_series.affx)
.
So in short; don't be worried too much by the distribution looking the same
whilst the biology is different. I am comparing in vitro vs. orthotopic
injection and although in excess of 10% of the genes differ SIGNIFICANTLY by
RMA, GC-RMA or PLIER the global expression distributions (i.e. box-plots)
look very similar after normalisation.
As I don't know why you are doing what you're doing: keep in mind that for
LOW expressors any of these global "equalisers" seem to introduce differing
degrees of artefactual gene-linkage which is why you should either ignore
the low-expression end of the spectrum and why some people still like to use
MASS5 when studying pathway-linkage. How much is low and various
work-arounds are the subject of plenty publications. No doubt this will keep
statisticians of the street for a while yet.
Bets regards,
Nick
N.V. Henriquez, Senior Research Associate
Dept. Of Neurodegenerative Diseases
Institute of Neurology, UCL,
Queen Square House rm 124
Queen Square
London WC1N 3BG
Tel. +44 2078373611 ext. 4150
Fax +44 2076762157
------------------------------
Message: 13
Date: Fri, 20 Jun 2008 17:46:09 -0700
From: "Siobhan A. Braybrook" <sabraybrook at ucdavis.edu>
Subject: [BioC] Normalization Recommendations- severe biological
variation
To: bioconductor at stat.math.ethz.ch
Message-ID: <6.1.2.0.2.20080620174011.053cab40 at mail.ucdavis.edu>
Content-Type: text/plain
Hello All!
I am hoping that someone might have some suggestions for a normalization
method when some of the samples in an experiment are very divergent due to
biology, not artifact.
I have tried out several (loess, rma, vsn, quantile) but I am worried by
how similar the distributions look afterwards.
It would be best to use a set of 'housekeeping genes'? The normal ones
(rRNA, gapdh, actin, etc) all are biologically different in these
treatments too (think dying tissue).
Formally we were using mas5 type summarization......but since it isn't the
most robust I wanted to try some other methods out. Is the mas5 type of
constant normalization really the best for this type of data and I am
chasing my tail?
Thanks for any advice!
Siobhan
S. A. Braybrook
Graduate Student, Harada Lab
Section of Plant Biology
University of California, Davis
Davis, CA 95616
Ph 530.752.6980
The time is always right, to do what is right.
- Martin Luther King, Jr.
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